CN101995380B - Method for monitoring soil petroleum pollution based on hyperspectral vegetation index - Google Patents
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Abstract
一种基于高光谱植被指数监测土壤石油污染的方法属于环境监测领域，本发明通过以下技术方案来实现：1)建立土壤总石油烃含量高光谱预测模型，包括以下步骤：选择采样点；高光谱测定；土壤取样和总石油烃含量测定；计算植被指数；确定最佳预测模型；2)土壤总石油烃含量高光谱预测模型的集成。本发明的有益效果为：相对于传统的土壤、植被监测方法，本发明简单易行，可节约大量的人力、财力和时间，对植被破坏小；结合航空、航天等遥感技术，可实现土壤石油污染的定时、定位、定量、大面积监测。
A method for monitoring soil oil pollution based on hyperspectral vegetation index belongs to the field of environmental monitoring. The present invention is realized through the following technical solutions: 1) establishing a hyperspectral prediction model for total petroleum hydrocarbon content in soil, comprising the following steps: selecting sampling points; determination; soil sampling and determination of total petroleum hydrocarbon content; calculation of vegetation index; determination of the best prediction model; 2) integration of hyperspectral prediction models for soil total petroleum hydrocarbon content. The beneficial effects of the present invention are: compared with traditional soil and vegetation monitoring methods, the present invention is simple and easy to implement, can save a lot of manpower, financial resources and time, and has little damage to vegetation; combined with remote sensing technologies such as aviation and spaceflight, it can realize soil petroleum monitoring. Timing, positioning, quantification, and large-scale monitoring of pollution.
Description
技术领域 technical field
本发明属于环境监测领域，具体涉及高光谱遥感在土壤石油污染监测中的应用。The invention belongs to the field of environmental monitoring, and in particular relates to the application of hyperspectral remote sensing in soil oil pollution monitoring.
背景技术 Background technique
伴随着石油的工业化生产和利用，石油污染已经成为一个严重的环境问题。石油进入生态系统后，不仅对生态系统的结构、功能产生较大影响，而且石油污染物可通过食物链在动植物体内逐级富集，最终进入人体，危害人类健康。With the industrial production and utilization of oil, oil pollution has become a serious environmental problem. After oil enters the ecosystem, it not only has a great impact on the structure and function of the ecosystem, but also oil pollutants can be gradually enriched in animals and plants through the food chain, and finally enter the human body, endangering human health.
全球石油总产量中，约80％是由陆地油田生产的。我国生产的原油也大部分出自陆上油田。因此，如何有效地进行陆地生态系统中石油污染的环境监测对于预防石油污染的扩散，高效开展石油污染的降解和修复工作，全面治理石油污染具有重要意义。About 80% of the world's total oil production is produced on land. Most of the crude oil produced in my country comes from onshore oil fields. Therefore, how to effectively conduct environmental monitoring of oil pollution in terrestrial ecosystems is of great significance to prevent the spread of oil pollution, efficiently carry out degradation and restoration of oil pollution, and comprehensively control oil pollution.
目前，陆地石油污染的环境监测，一般进行常规的土壤、植被监测。但是，土壤和植被监测，往往需要大量的取样，样品的分析测定需要大量的仪器，测定程序也较为复杂，因此是一项耗力、耗财、耗时的工作。过去的研究表明，石油污染会影响植物的叶面积指数、生物量、植被盖度、光合色素等生理生化指标。而这些指标的变化可以利用植被指数进行有效地监测。同时，相对于传统的宽波段遥感技术，高光谱成像光谱仪在可见光-近红外区域的光谱分辨率可达到纳米级，因此，可以取得研究对象详细而精确的光谱信息。从而为植被指数的计算提供了更多的选择空间，使植被指数监测的敏感性和准确性进一步提高。基于上述原因，本发明针对以芦苇为优势种的生态系统，利用野外获得的植被高光谱数据，计算高光谱植被指数，最终建立了芦苇生态系统土壤石油污染的高光谱预测模型。At present, the environmental monitoring of oil pollution on land generally carries out routine soil and vegetation monitoring. However, soil and vegetation monitoring often requires a large number of samples, and the analysis and determination of samples requires a large number of instruments, and the measurement procedures are relatively complicated, so it is a labor-intensive, money-consuming, and time-consuming task. Past studies have shown that oil pollution can affect the physiological and biochemical indicators of plants such as leaf area index, biomass, vegetation coverage, and photosynthetic pigments. The changes of these indicators can be effectively monitored by using the vegetation index. At the same time, compared with the traditional broadband remote sensing technology, the spectral resolution of the hyperspectral imaging spectrometer in the visible light-near infrared region can reach the nanometer level. Therefore, detailed and accurate spectral information of the research object can be obtained. Therefore, more choices are provided for the calculation of vegetation index, and the sensitivity and accuracy of vegetation index monitoring are further improved. Based on the above reasons, the present invention uses vegetation hyperspectral data obtained in the field to calculate the hyperspectral vegetation index for the ecosystem with reed as the dominant species, and finally establishes a hyperspectral prediction model for soil oil pollution in the reed ecosystem.
发明内容Contents of the invention
本发明的目的是提供一种基于高光谱植被指数监测土壤石油污染的方法，以克服传统的土壤、植被监测方法耗力、耗财、耗时的不足。The purpose of the present invention is to provide a method for monitoring soil oil pollution based on hyperspectral vegetation index, so as to overcome the deficiencies of traditional soil and vegetation monitoring methods that are labor-intensive, financially and time-consuming.
本发明的目的通过以下技术方案来实现：The purpose of the present invention is achieved through the following technical solutions:
1)建立土壤总石油烃含量高光谱预测模型，包括以下步骤：1) Establish a hyperspectral prediction model for soil total petroleum hydrocarbon content, including the following steps:
(a)选择采样点：在油井周围的芦苇植被上选择30个采样点，各样点距油井的距离在30-130m之间，采样点未遭受人为干扰，植被未遭践踏，且采样点不存在非石油污染胁迫；(a) Select sampling points: select 30 sampling points on the reed vegetation around the oil well. There is a non-oil pollution threat;
(b)高光谱测定：采用便携式地物波谱仪在经过步骤(a)所选采样点上测定芦苇植被的高光谱数据，测定在每天上午的10-12时之间进行，测定时天空晴朗无云；每个采样点上重复测定10次，获取10条高光谱曲线，删除与其他曲线存在明显差异的异常曲线，再计算每个采样点剩余高光谱曲线的平均值，得到每个采样点芦苇植被的高光谱曲线；(b) hyperspectral measurement: adopt portable object spectrometer to measure the hyperspectral data of reed vegetation on the selected sampling point through step (a), measure and carry out between 10-12 o'clock in the morning every day, the sky is clear during measurement. Cloud; repeat the measurement 10 times at each sampling point, obtain 10 hyperspectral curves, delete the abnormal curves that are significantly different from other curves, and then calculate the average value of the remaining hyperspectral curves at each sampling point to obtain the reed Hyperspectral curves of vegetation;
(c)土壤取样和总石油烃含量测定：在各采样点处取深度为0-30cm的土壤，土壤取样量重450-550g，然后采用红外分光光度法测定每个采样点土壤总石油烃含量，土壤总石油烃含量的单位为mg/kg；(c) Soil sampling and determination of total petroleum hydrocarbon content: take soil with a depth of 0-30cm at each sampling point, the soil sampling weight is 450-550g, and then use infrared spectrophotometry to measure the soil total petroleum hydrocarbon content of each sampling point , the unit of soil total petroleum hydrocarbon content is mg/kg;
(d)计算植被指数：根据步骤(b)所得芦苇植被的高光谱曲线数据，计算出每个采样点的44种植被指数；(d) calculate the vegetation index: according to the hyperspectral curve data of step (b) gained reed vegetation, calculate 44 kinds of vegetation indexes of each sampling point;
(e)确定最佳预测模型：分别采用线性、对数、倒数、二次、三次、幂、S型曲线、指数函数模型对每个采样点44种植被指数与该采样点土壤总石油烃含量的关系进行拟合；确定最佳预测模型为TPH＝0.131/RES，其中TPH为土壤总石油烃含量，土壤总石油烃含量的单位为mg/kg；RES为红边斜率，即波长在680-750nm范围内光谱反射率一阶微分的最大值；(e) Determine the best forecasting model: use linear, logarithmic, reciprocal, quadratic, cubic, power, S-curve, and exponential function models to compare the 44 vegetation indices of each sampling point with the total petroleum hydrocarbon content of the soil at the sampling point Fitting the relationship; determine the best prediction model as TPH=0.131/RES, where TPH is the total petroleum hydrocarbon content of the soil, and the unit of the total petroleum hydrocarbon content of the soil is mg/kg; RES is the slope of the red edge, that is, the wavelength is between 680- The maximum value of the first order differential of spectral reflectance in the range of 750nm;
2)土壤总石油烃含量高光谱预测模型的集成：将经过步骤1)所得预测模型集成到地物波谱仪的随机软件中，实现土壤石油污染的实时定位和定量监测；结合航空、航天遥感技术，进一步实现土壤石油污染的大面积快速监测。2) Integration of hyperspectral prediction model for soil total petroleum hydrocarbon content: integrate the prediction model obtained through step 1) into the random software of ground object spectrometer to realize real-time positioning and quantitative monitoring of soil oil pollution; combined with aviation and aerospace remote sensing technology , to further realize large-area rapid monitoring of soil oil pollution.
本发明的有益效果为：相对于传统的土壤、植被监测方法，本发明简单易行，可节约大量的人力、财力和时间，对植被破坏小；结合航空、航天等遥感技术，可实现土壤石油污染的定时、定位、定量、大面积监测。The beneficial effects of the present invention are: compared with traditional soil and vegetation monitoring methods, the present invention is simple and easy to implement, can save a lot of manpower, financial resources and time, and has little damage to vegetation; combined with remote sensing technologies such as aviation and spaceflight, it can realize soil petroleum monitoring. Timing, positioning, quantification, and large-scale monitoring of pollution.
附图说明 Description of drawings
图1是本发明实施例所述的一种基于高光谱植被指数监测土壤石油污染的方法中植被指数红边斜率与土壤总石油烃含量的关系曲线图。Fig. 1 is a graph of the relationship between the slope of the red edge of the vegetation index and the total petroleum hydrocarbon content of the soil in a method for monitoring soil oil pollution based on a hyperspectral vegetation index described in an embodiment of the present invention.
具体实施方式 Detailed ways
本发明实施例所述的一种基于高光谱植被指数监测土壤石油污染的方法，包括以下步骤：A method for monitoring soil oil pollution based on a hyperspectral vegetation index described in an embodiment of the present invention comprises the following steps:
1)建立土壤总石油烃含量高光谱预测模型，包括以下步骤：1) Establish a hyperspectral prediction model for soil total petroleum hydrocarbon content, including the following steps:
(a)选择采样点：在油井周围的芦苇植被上选择30个采样点，各样点距油井的距离在30-130m之间，采样点未遭受人为干扰，植被未遭践踏，且采样点不存在非石油污染胁迫；(a) Select sampling points: select 30 sampling points on the reed vegetation around the oil well. There is a non-oil pollution threat;
(b)高光谱测定：采用便携式地物波谱仪在经过步骤(a)所选采样点上测定芦苇植被的高光谱数据，测定在每天上午的10-12时之间进行，测定时天空晴朗无云；每个采样点上重复测定10次，获取10条高光谱曲线，删除与其他曲线存在明显差异的异常曲线，再计算每个采样点剩余高光谱曲线的平均值，得到每个采样点芦苇植被的高光谱曲线；(b) hyperspectral measurement: adopt portable object spectrometer to measure the hyperspectral data of reed vegetation on the selected sampling point through step (a), measure and carry out between 10-12 o'clock in the morning every day, the sky is clear during measurement. Cloud; repeat the measurement 10 times at each sampling point, obtain 10 hyperspectral curves, delete the abnormal curves that are significantly different from other curves, and then calculate the average value of the remaining hyperspectral curves at each sampling point to obtain the reed Hyperspectral curves of vegetation;
(c)土壤取样和总石油烃含量测定：在各采样点处取深度为0-30cm的土壤，土壤取样量重450-550g，然后采用红外分光光度法测定每个采样点土壤总石油烃含量，土壤总石油烃含量的单位为mg/kg；(c) Soil sampling and determination of total petroleum hydrocarbon content: take soil with a depth of 0-30cm at each sampling point, the soil sampling weight is 450-550g, and then use infrared spectrophotometry to measure the soil total petroleum hydrocarbon content of each sampling point , the unit of soil total petroleum hydrocarbon content is mg/kg;
(d)计算植被指数：根据步骤(b)所得芦苇植被的高光谱曲线数据，计算出每个采样点的44种植被指数；(d) calculate the vegetation index: according to the hyperspectral curve data of step (b) gained reed vegetation, calculate 44 kinds of vegetation indexes of each sampling point;
(e)确定最佳预测模型：分别采用线性、对数、倒数、二次、三次、幂、S型曲线、指数函数模型对每个采样点44种植被指数与该采样点土壤总石油烃含量的关系进行拟合；确定最佳预测模型为TPH＝0.131/RES，其中TPH为土壤总石油烃含量，土壤总石油烃含量的单位为mg/kg；RES为红边斜率，即波长在680-750nm范围内光谱反射率一阶微分的最大值；(e) Determine the best forecasting model: use linear, logarithmic, reciprocal, quadratic, cubic, power, S-curve, and exponential function models to compare the 44 vegetation indices of each sampling point with the total petroleum hydrocarbon content of the soil at the sampling point Fitting the relationship; determine the best prediction model as TPH=0.131/RES, where TPH is the total petroleum hydrocarbon content of the soil, and the unit of the total petroleum hydrocarbon content of the soil is mg/kg; RES is the slope of the red edge, that is, the wavelength is between 680- The maximum value of the first order differential of spectral reflectance in the range of 750nm;
2)土壤总石油烃含量高光谱预测模型的集成：将经过步骤1)所得预测模型集成到地物波谱仪的随机软件中，实现土壤石油污染的实时定位和定量监测；结合航空、航天遥感技术，进一步实现土壤石油污染的大面积快速监测。2) Integration of hyperspectral prediction model for soil total petroleum hydrocarbon content: integrate the prediction model obtained through step 1) into the random software of ground object spectrometer to realize real-time positioning and quantitative monitoring of soil oil pollution; combined with aviation and aerospace remote sensing technology , to further realize large-area rapid monitoring of soil oil pollution.
在上述基于高光谱植被指数监测土壤石油污染的方法中，光谱测定时，便携式地物波谱仪采用美国ASD公司(美国光谱分析仪器公司)生产的FieldSpec3便携式地物波谱仪。FieldSpec3便携式地物波谱仪适用于遥感测量，农作物监测，森林研究，工业照明测量，海洋学研究和矿物勘察的各方面。该仪器重量轻便，可实时测量并观察反射、透射、辐射度光谱曲线；可以实时显示绝对反射比；具有高信噪比、高可靠性、高重复性等优点。该仪器可测定350-2500nm波长范围的光谱，光谱分辨率为3-10nm。In the method for monitoring soil oil pollution based on hyperspectral vegetation index, the portable surface object spectrometer adopts the FieldSpec3 portable surface object spectrometer produced by American ASD Company (American Spectral Analysis Instrument Company) during the spectrum measurement. The FieldSpec3 portable ground object spectrometer is suitable for all aspects of remote sensing surveys, crop monitoring, forest research, industrial lighting surveys, oceanographic research and mineral exploration. The instrument is light in weight, and can measure and observe reflection, transmission, and radiance spectral curves in real time; it can display absolute reflectance in real time; it has the advantages of high signal-to-noise ratio, high reliability, and high repeatability. The instrument can measure the spectrum in the wavelength range of 350-2500nm, and the spectral resolution is 3-10nm.
在上述基于高光谱植被指数监测土壤石油污染的方法中，计算植被指数时，本发明选择了现有的44种植被指数，各植被指数及其计算公式见表1。由于很多植被指数最初是以宽波段光谱反射率计算的，本发明中以相应波段内敏感波长的光谱反射率代替。In the method for monitoring soil oil pollution based on the hyperspectral vegetation index, when calculating the vegetation index, the present invention selects 44 existing vegetation indexes, and each vegetation index and its calculation formula are shown in Table 1. Since many vegetation indices are initially calculated by wide-band spectral reflectance, in the present invention, they are replaced by the spectral reflectance of sensitive wavelengths in the corresponding band.
表1本发明中使用的植被指数Vegetation index used in the present invention in table 1
续表1本发明中使用的植被指数Continuation of the vegetation index used in the present invention in table 1
续表1本发明中使用的植被指数Continuation of the vegetation index used in the present invention in table 1
续表1本发明中使用的植被指数Continuation of the vegetation index used in the present invention in table 1
在上述基于高光谱植被指数监测土壤石油污染的方法中，确定最佳预测模型时，分别采用线性、对数、倒数、二次、三次、幂、S型曲线、指数等函数模型对各个植被指数与土壤总石油烃含量的关系进行了拟合。拟合结果见表2。In the above method of monitoring soil oil pollution based on hyperspectral vegetation index, when determining the best prediction model, the linear, logarithmic, reciprocal, quadratic, cubic, power, S-curve, exponential and other functional models are respectively used to analyze each vegetation index. The relationship with the total petroleum hydrocarbon content of the soil was fitted. The fitting results are shown in Table 2.
表2各函数模型对植被指数的拟合结果(n＝30)Table 2 Fitting results of each function model to vegetation index (n=30)
续表2各函数模型对植被指数的拟合结果(n＝30)Continued Table 2 Fitting results of each function model to vegetation index (n=30)
有些植被指数的计算结果中存在负值，因此无法获得该植被指数某些函数模型的拟合结果，在表中以缺失值符号“-”表示。There are negative values in the calculation results of some vegetation indexes, so the fitting results of some function models of the vegetation indexes cannot be obtained, and the missing value symbol "-" is indicated in the table.
表2中相关指数R2＞0.85的预测模型见表3。比较各预测模型，可确定TPH＝0.131/RES为最佳预测模型，其模型如图1所示。其中TPH为土壤总石油烃含量，土壤总石油烃含量的单位为mg/kg；RES为红边斜率，即680nm-750nm波长范围内光谱反射率一阶微分的最大值。该预测模型的R2高达0.948，p值也远小于0.01，表明该模型预测的可靠程度较高。今后，可将该预测模型集成到地物波谱仪的随机软件中，实现土壤石油污染的定位、实时、定量监测。结合航空、航天等遥感技术，可进一步实现土壤石油污染的大面积快速监测。该方法相对于传统的土壤、植被监测，简单易行，可节约大量的人力、财力和时间，对植被破坏小。See Table 3 for the prediction model of the correlation index R 2 >0.85 in Table 2. Comparing the prediction models, it can be determined that TPH=0.131/RES is the best prediction model, and its model is shown in Figure 1. Among them, TPH is the total petroleum hydrocarbon content of soil, and the unit of soil total petroleum hydrocarbon content is mg/kg; RES is the red edge slope, that is, the maximum value of the first order differential of spectral reflectance in the wavelength range of 680nm-750nm. The R 2 of this prediction model is as high as 0.948, and the p value is also much less than 0.01, indicating that the reliability of the prediction of this model is relatively high. In the future, the prediction model can be integrated into the random software of the surface object spectrometer to realize the positioning, real-time and quantitative monitoring of soil oil pollution. Combined with remote sensing technologies such as aviation and aerospace, it can further realize large-scale and rapid monitoring of soil oil pollution. Compared with traditional soil and vegetation monitoring, this method is simple and easy to implement, can save a lot of manpower, financial resources and time, and has little damage to vegetation.
表3相关指数R2＞0.85的预测模型及检验(n＝30)Table 3 Prediction model and test of correlation index R 2 >0.85 (n=30)
采用本发明监测土壤石油污染时，由于生态系统的不同，反映生态系统中土壤遭受石油污染程度的植被指数可能不同；因此针对其他生态系统，可以利用别的植被指数(包括利用其他敏感波长计算的植被指数)结合其他函数模型提出新的光谱预测模型。When adopting the present invention to monitor soil oil pollution, due to the difference of ecosystems, the vegetation index reflecting the oil pollution degree of soil in the ecosystem may be different; therefore for other ecosystems, other vegetation indices (including utilizing other sensitive wavelengths to calculate Vegetation index) combined with other functional models to propose a new spectral prediction model.
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| CN102680413B (en) * | 2012-05-25 | 2014-02-19 | 浙江大学 | Device and method for rapid detection of field soil organic matter content by panoramic ring hyperspectral |
| CN103146805B (en) * | 2013-03-07 | 2014-05-28 | 中国水产科学研究院黄海水产研究所 | Shellfish monitoring method for ocean oil spill pollution base on integration biomarker method |
| CN105527230A (en) * | 2015-12-04 | 2016-04-27 | 成都理工大学 | High vegetation coverage area remote sensing prospecting method based on near-infrared band 670- |
| CN109543654B (en) * | 2018-12-14 | 2023-04-18 | 常州大学 | Construction method of improved vegetation index reflecting crop growth conditions |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107870150A (en) * | 2017-11-02 | 2018-04-03 | 北京师范大学 | Hyperspectral inversion method of soil parameters based on heavy metals in soils in hydrofluctuation zone |
| CN107870150B (en) * | 2017-11-02 | 2019-03-29 | 北京师范大学 | Soil parameters EO-1 hyperion inversion method based on falling zone heavy metal-polluted soil |
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